AgiMicroRna Pedro Lopez - Romero April

AgiMicroRna provides useful functionality for the processing, quality assessment and differential expression analysis of Agilent microRNA array data. The package uses a limma-like structure to generate the processed data in order to make statistical inferences about differential expression using the linear model features implemented in limma. Standard Bioconductor objects are used so that other packages could be used as well. AgiMicroRna reads into R [12] the scanned data exported by the Agilent Feature Extraction (AFE) image analysis software [1]. Standard graphical utilities can be used to evaluate the quality of the data. AgiMicroRna includes a full data example that can be loaded into R in order to illustrate the capabilities of the package. The data come from human mesenchymal stem cells obtained from bone marrow. 100 ng of each RNA sample were hybridized onto Agilent Human microRNA Microarray v2.0 (G4470B, Agilent Technologies). The Human microRNA microarray v2.0 contains 723 human and 76 human viral microRNAs, each of them replicated 16 times. There are 362 microRNAs interrogated by 2 different oligonucleotides, 45 microRNAs by 3 and 390 microRNAs interrogated by 4 different oligonucleotides. Only 2 microRNAs are interrogated by the same oligonucleotide. The array contains also a set of positive and negative controls that are replicated a different number of times. For the statistical analysis we need an estimate of the expression measure for every microRNA that has to be normalized between arrays. This processed signal is going to be used to make statistical inferences about the differential expression. In AgiMicroRna the processed microRNA signal can be obtained using two different protocols. The first uses the Total Gene Signal (TGS) computed by the AFE algorithm [1] whereas the second obtains an estimate of the gene signal using the RMA algorithm [8]. In more detail, the data processing for the first protocol is accomplished according to the following sequential steps: 1) Obtaining the Total microRNA Gene Signal processed by AFE, 2) normalization between arrays. For the RMA algorithm, the steps are slightly different: 1) The signal is background corrected using the exponential + normal convolution model, 2) the background signal